| On First-Order Meta-Learning Algorithms A Nichol, J Achiam, J Schulman arXiv preprint arXiv:1803.02999, 2018 | 1536 | 2018 |
| Constrained Policy Optimization J Achiam, D Held, A Tamar, P Abbeel arXiv preprint arXiv:1705.10528, 2017 | 886 | 2017 |
| Benchmarking Safe Exploration in Deep Reinforcement Learning A Ray, J Achiam, D Amodei https://cdn.openai.com/safexp-short.pdf, 2019 | 210 | 2019 |
| Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning J Achiam, S Sastry arXiv preprint arXiv:1703.01732, 2017 | 202 | 2017 |
| Spinning Up in Deep Reinforcement Learning J Achiam https://spinningup.openai.com, 0 | 185 | |
| Variational Option Discovery Algorithms J Achiam, H Edwards, D Amodei, P Abbeel arXiv preprint arXiv:1807.10299, 2018 | 131 | 2018 |
| Responsive safety in reinforcement learning by pid lagrangian methods A Stooke, J Achiam, P Abbeel International Conference on Machine Learning, 9133-9143, 2020 | 108 | 2020 |
| Towards Characterizing Divergence in Deep Q-Learning J Achiam, E Knight, P Abbeel arXiv preprint arXiv:1903.08894, 2019 | 77 | 2019 |
| Advanced Policy Gradient Methods J Achiam Lecture [online] http://rail.eecs.berkeley.edu/deeprlcourse-fa17/f17docs …, 2017 | 3 | 2017 |
| Exploration and Safety in Deep Reinforcement Learning JS Achiam University of California, Berkeley, 2021 | 1 | 2021 |
| Training Dynamics Models for Accurate Long-Horizon Prediction E Knight, J Achiam, UC OpenAI | | |
